Classification of aggressive behaviour in pigs by activity index and multilayer feed forward neural network

Oczak, Maciej; Viazzi, Stefano; Ismayilova, Gunel; Sonoda, Lilia Thays; Roulston, N.; Fels, Michaela; Bahr, Claudia; Hartung, J.; Guarino, Marcella; Berckmans, Daniël; Vranken, Erik

doi:10.1016/j.biosystemseng.2014.01.005

Cited by 80 publications

(44 citation statements)

References 18 publications

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“…In order to combine advantages of group analysis and 3D cameras, this work attempts to recognise aggressive pig behaviours on group level by using a depth sensor. In the previous studies of aggressive behaviours of pigs based on group analysis Oczak et al, 2014), all pigs' moving pixels were considered. However, the moving pixels caused by non-aggressive pigs will influence the accuracy of the aggression recognition.…”

Section: Methodsmentioning

confidence: 99%

Proceedings of the Seventeenth Biennial Conference of the Australasian Pig Science Association (APSA)

2019

Advances in Animal Biosciences

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Section: Methodsmentioning

confidence: 99%

Proceedings of the Seventeenth Biennial Conference of the Australasian Pig Science Association (APSA)

2019

Advances in Animal Biosciences

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“…Bringing the animals closer to the farmer Oczak et al (2014) and Chen et al (2017) classified the aggressive behaviour into high-and medium-aggression among pigs. However, only using the high and medium intensity for definition and recognition of aggressive behaviours will be subject to greater interference by other behaviours.…”

Section: Image Analysismentioning

confidence: 99%

“…This is a part of the image analysis process that needs considerable consideration. In the case of pig aggression, features of mean intensity and occupation index Oczak et al, 2014) were found to be useful in the past. Later, the acceleration feature (Chen et al, 2017) and motion features with higher discrimination (e.g.…”

Section: Image Analysismentioning

confidence: 99%

Review: Precision livestock farming: building ‘digital representations’ to bring the animals closer to the farmer

Norton

Chen

Larsen

et al. 2019

Animal

120

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Economic pressures continue to mount on modern-day livestock farmers, forcing them to increase herds sizes in order to be commercially viable. The natural consequence of this is to drive the farmer and the animal further apart. However, closer attention to the animal not only positively impacts animal welfare and health but can also increase the capacity of the farmer to achieve a more sustainable production. State-of-the-art precision livestock farming (PLF) technology is one such means of bringing the animals closer to the farmer in the facing of expanding systems. Contrary to some current opinions, it can offer an alternative philosophy to 'farming by numbers'. This review addresses the key technology-oriented approaches to monitor animals and demonstrates how image and sound analyses can be used to build 'digital representations' of animals by giving an overview of some of the core concepts of PLF tool development and value discovery during PLF implementation. The key to developing such a representation is by measuring important behaviours and events in the livestock buildings. The application of image and sound can realise more advanced applications and has enormous potential in the industry. In the end, the importance lies in the accuracy of the developed PLF applications in the commercial farming system as this will also make the farmer embrace the technological development and ensure progress within the PLF field in favour of the livestock animals and their well-being. ImplicationsThis work address the main approaches utilised in developing precision livestock farming tools. Precision livestock farming is an approach that enables the farmer with more objective information about the animal to make better choices about the sustainability of their production system. This paper demonstrates some of the key solutions and the approaches taken to develop technologies with sound and image analyses. †

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“…However, these movements are not always attributed to aggressive behaviour and may indicate other behaviours such as playing and chasing (Viazzi et al., 2014). Leveraging a similar approach based on pixel differences, it is possible to extract detail about aggression, such as low, medium, and high level (Oczak et al., 2014). A method to automatically detect head-to-head (or body) knocking and chasing has been demonstrated with a 3D camera (Lee et al., 2016).…”

Section: Sensors For Measuring Pig Behaviourmentioning

confidence: 99%

Early detection of health and welfare compromises through automated detection of behavioural changes in pigs

Matthews

Miller

Clapp

et al. 2016

The Veterinary Journal

196

129

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HighlightsRecent developments in automatically detecting compromised pig health and welfare.Five categories of behaviour are reviewed.Behaviours mapped to sensors that are feasible for automated detection.Progress towards levels of automation through detection, monitoring and fully automatic detection of behavioural change.Challenges for automated detection of behavioural changes are multifaceted and require trade-offs to develop such systems.

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Classification of aggressive behaviour in pigs by activity index and multilayer feed forward neural network

Cited by 80 publications

References 18 publications

Proceedings of the Seventeenth Biennial Conference of the Australasian Pig Science Association (APSA)

Proceedings of the Seventeenth Biennial Conference of the Australasian Pig Science Association (APSA)

Review: Precision livestock farming: building ‘digital representations’ to bring the animals closer to the farmer

Early detection of health and welfare compromises through automated detection of behavioural changes in pigs

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